Statistical Analysis of Agricultural Experiments using R
Preface
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1 Introduction
2 Descriptive Statistics
3 Simple Correlation
4 Student’s t-test
5 Analysis of Variance (ANOVA)
- 5.1 One-Way Analysis of Variance
- 5.2 Multi-Factor ANOVA
  - 5.2.1 Mean Separation
6 Linear Regression
- 6.1 Simple Linear Regression
  - 6.1.1 Model Diagnostics
  - 6.1.2 Model Interpretation
7 Analysis of Covariance (ANCOVA)
- 7.1 Example 1: Mechanical Weed Control
- 7.2 Example 2: Duration of competition
8 Mixed Models - ANOVA
- 8.1 Mixed Effects Model using the lme4 Package
9 Mixed Models - Regression
- 9.1 Regression Models with Mixed Effects
  - 9.1.1 Example 1: Sugarbeet yield
  - 9.1.2 Example 2: Fungicide toxicity
10 Logistic Regression (Binary Response)
- 10.1 Generalized Linear Model
11 Dose-Response curves
12 Nonlinear Regression - Selectivity of Herbicides
13 Plant Competition Experiments
- 13.1 Yield, Density and Yield Loss
- 13.2 Replacement Series
14 Seed Germination
- 14.1 Example 1: Chickweed
- 14.2 Example 2: Bromus tectorum
15 Visualization
- 15.1 Base R Graphics
- 15.2 ggplot2
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Statistical Analysis of Agricultural Experiments using R

Statistical Analysis of Agricultural Experiments using R

Andrew Kniss & Jens Streibig

Last Updated on 2019-10-26

Kniss AR, Streibig JC (2018) Statistical Analysis of Agricultural Experiments using R. https://Rstats4ag.org. Accessed